scholarly journals An assessment of the ability of Bartlett–Lewis type of rainfall models to reproduce drought statistics

2013 ◽  
Vol 17 (12) ◽  
pp. 5167-5183 ◽  
Author(s):  
M. T. Pham ◽  
W. J. Vanhaute ◽  
S. Vandenberghe ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Important Variable ◽  
Drought Severity ◽  
Test Case ◽  
Rainfall Time Series ◽  
Climate Data ◽  
Drought Duration ◽  
Environmental Consequences ◽  
Stochastic Point Process ◽  
The Impact

Abstract. Of all natural disasters, the economic and environmental consequences of droughts are among the highest because of their longevity and widespread spatial extent. Because of their extreme behaviour, studying droughts generally requires long time series of historical climate data. Rainfall is a very important variable for calculating drought statistics, for quantifying historical droughts or for assessing the impact on other hydrological (e.g. water stage in rivers) or agricultural (e.g. irrigation requirements) variables. Unfortunately, time series of historical observations are often too short for such assessments. To circumvent this, one may rely on the synthetic rainfall time series from stochastic point process rainfall models, such as Bartlett–Lewis models. The present study investigates whether drought statistics are preserved when simulating rainfall with Bartlett–Lewis models. Therefore, a 105 yr 10 min rainfall time series obtained at Uccle, Belgium is used as a test case. First, drought events were identified on the basis of the Effective Drought Index (EDI), and each event was characterized by two variables, i.e. drought duration (D) and drought severity (S). As both parameters are interdependent, a multivariate distribution function, which makes use of a copula, was fitted. Based on the copula, four types of drought return periods are calculated for observed as well as simulated droughts and are used to evaluate the ability of the rainfall models to simulate drought events with the appropriate characteristics. Overall, all Bartlett–Lewis model types studied fail to preserve extreme drought statistics, which is attributed to the model structure and to the model stationarity caused by maintaining the same parameter set during the whole simulation period.

scholarly journals A copula-based assessment of Bartlett–Lewis type of rainfall models for preserving drought statistics

2013 ◽  
Vol 10 (6) ◽  
pp. 7469-7516 ◽  
Author(s):  
M. T. Pham ◽  
W. J. Vanhaute ◽  
S. Vandenberghe ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Important Variable ◽  
Drought Severity ◽  
Test Case ◽  
Rainfall Time Series ◽  
Climate Data ◽  
Drought Duration ◽  
Environmental Consequences ◽  
Stochastic Point Process ◽  
The Impact

Abstract. Of all natural disasters, the economic and environmental consequences of droughts are among the highest because of their longevity and widespread spatial extent. Because of their extreme behaviour, studying droughts generally requires long time series of historical climate data. Rainfall is a very important variable for calculating drought statistics, for quantifying historical droughts or for assessing the impact on other hydrological (e.g. water stage in rivers) or agricultural (e.g. irrigation requirements) variables. Unfortunately, time series of historical observations are often too short for such assessments. To circumvent this, one may rely on the synthetic rainfall time series from stochastic point process rainfall models, such as Bartlett–Lewis models. The present study investigates whether drought statistics are preserved when simulating rainfall with Bartlett–Lewis models. Therefore, a 105 yr 10 min rainfall time series obtained at Uccle, Belgium is used as test case. First, drought events were identified on the basis of the Effective Drought Index (EDI), and each event was characterized by two variables, i.e. drought duration (D) and drought severity (S). As both parameters are interdependent, a multivariate distribution function, which makes use of a copula, was fitted. Based on the copula, four types of drought return periods are calculated for observed as well as simulated droughts and are used to evaluate the ability of the rainfall models to simulate drought events with the appropriate characteristics. Overall, all Bartlett–Lewis type of models studied fail in preserving extreme drought statistics, which is attributed to the model structure and to the model stationarity caused by maintaining the same parameter set during the whole simulation period.

scholarly journals Modeling, prediction and trend assessment of drought in Iran using standardized precipitation index

2018 ◽  
Vol 10 (1) ◽  
pp. 181-196 ◽  
Author(s):  
Mehdi Bahrami ◽  
Samira Bazrkar ◽  
Abdol Rassoul Zarei
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Occurrence Frequency ◽  
Natural Phenomenon ◽  
Drought Severity ◽  
Data Series ◽  
Climate Data ◽  
Seasonal Drought

Abstract Drought as an exigent natural phenomenon, with high frequency in arid and semi-arid regions, leads to enormous damage to agriculture, economy, and environment. In this study, the seasonal Standardized Precipitation Index (SPI) drought index and time series models were employed to model and predict seasonal drought using climate data of 38 Iranian synoptic stations during 1967–2014. In order to model and predict seasonal drought ITSM (Interactive Time Series Modeling) statistical software was used. According to the calculated seasonal SPI, within the study area, drought severity classes 4 and 3 had the greatest occurrence frequency, while classes 6 and 7 had the least occurrence frequency. Results indicated that the best fitted models were Moving-Average or MA (5) Innovations and MA (5) Hannan-Rissenen, with 60.53 and 15.79 percentage, respectively. On the other hand, results of the prediction as well, indicated that drought class 4 with the highest percentages, was the most abundant class over the study area and drought class 7 was the least frequent class. According to results of trend analysis, without attention to significance of them, observed seasonal SPI data series (1967–2014), in 84.21% of synoptic stations had a negative trend, but this percentage changes to 86.84% when studying the combination of observed and predicted simultaneously (1967–2019).

scholarly journals Climatological Drought Forecasting Using Bias Corrected CMIP6 Climate Data: A Case Study for India

Forecasting ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 59-84 ◽  
Author(s):  
Alen Shrestha ◽  
Md Mafuzur Rahaman ◽  
Ajay Kalra ◽  
Rohit Jogineedi ◽  
Pankaj Maheshwari
Keyword(s):  
Coupled Model ◽  
Severity Index ◽  
Extreme Drought ◽  
Drought Severity ◽  
Climate Data ◽  
Drought Forecasting ◽  
Drought Duration ◽  
Intercomparison Project ◽  
Near Future

This study forecasts and assesses drought situations in various regions of India (the Araveli region, the Bundelkhand region, and the Kansabati river basin) based on seven simulated climates in the near future (2015–2044). The self-calibrating Palmer Drought Severity Index (scPDSI) was used based on its fairness in identifying drought conditions that account for the temperature as well. Gridded temperature and rainfall data of spatial resolution of 1 km were used to bias correct the multi-model ensemble mean of the Global Climatic Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) project. Equidistant quantile-based mapping was adopted to remove the bias in the rainfall and temperature data, which were corrected on a monthly scale. The outcome of the forecast suggests multiple severe-to-extreme drought events of appreciable durations, mostly after the 2030s, under most climate scenarios in all the three study areas. The severe-to-extreme drought duration was found to last at least 20 to 30 months in the near future in all three study areas. A high-resolution drought index was developed and proven to be a key to assessing the drought situation.

scholarly journals Hydrological Drought Regimes of the Huai River Basin, China: Probabilistic Behavior, Causes and Implications

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2390 ◽  
Author(s):  
Sun ◽  
Zhang ◽  
Yao ◽  
Wen
Keyword(s):  
River Basin ◽  
Hydrological Drought ◽  
Drought Severity ◽  
Copula Functions ◽  
Drought Duration ◽  
Huai River Basin ◽  
Huai River ◽  
The Impact ◽  
Hydrological Droughts ◽  
The Huai River Basin

: Hydrological droughts were characterized using the run-length theory and the AIC (Akaike information criterion) techniques were accepted to evaluate the modeling performance of nine probability functions. In addition, the copula functions were used to describe joint probability behaviors of drought duration and drought severity for the major tributaries of the Huai River Basin (HRB) which is located in the transitional zone between humid and semi-humid climates. The results indicated that: (1) the frequency of hydrological droughts in the upper HRB is higher than that in the central HRB, while the duration of the hydrological drought is in reverse spatial pattern. The drought frequency across the Shiguan River along the south bank of the HRB is higher than the other two tributaries; (2) generalized Pareto distribution is the appropriate distribution function with the best performance in modelling the drought duration over the HRB; while the Generalized Extreme Value (GEV) distribution can effectively describe the probabilistic properties of the drought severity. Joe copula and Tawn copula functions are the best choices and were used in this study. Given return periods of droughts of <30 years, the droughts in the upper HRB are the longest, and the shortest are in the central HRB; (3) the frequency of droughts along the mainstream of the HRB is higher than tributaries of the HRB. However, concurrence probability of droughts along the mainstream of the HRB is lower than the tributaries of the HRB. The drought resistance capacity of HRB has been significantly improved, effectively reducing the impact of hydrological drought on crops after 2010.

scholarly journals Attribution Analysis of Hydrological Drought Risk Under Climate Change and Human Activities: A Case Study on Kuye River Basin in China

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1958 ◽  
Author(s):  
Zhang ◽  
Wang ◽  
Zhou
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Human Activities ◽  
Large Scale ◽  
Research Area ◽  
Drought Severity ◽  
Drought Risk ◽  
Drought Duration ◽  
Recurrence Period ◽  
The Impact

This study conducted quantitative diagnosis on the impact of climate change and human activities on drought risk. Taking the Kuye river basin (KRB) in China as the research area, we used variation point diagnosis, simulation of precipitation and runoff, drought risk assessment, and attribution quantification. The results show that: (1) the annual runoff sequence of KRB changed significantly after 1979, which was consistent with the introduction of large-scale coal mining; (2) under the same drought recurrence period, the drought duration and severity in the human activity stage were significantly worse than in the natural and simulation stages, indicating that human activities changed the drought risk in this area; and (3) human activities had little impact on drought severity in the short duration and low recurrence period, but had a greater impact in the long duration and high recurrence period. These results provide scientific guidance for the management, prevention, and resistance of drought; and guarantee sustainable economic and social development in the KRB.

scholarly journals Causal networks of biosphere–atmosphere interactions

2019 ◽  
Author(s):  
Christopher Krich ◽  
Jakob Runge ◽  
Diego G. Miralles ◽  
Mirco Migliavacca ◽  
Oscar Perez-Priego ◽  
...  
Keyword(s):  
Time Series ◽  
Observational Data ◽  
Large Scale ◽  
Terrestrial Ecosystem ◽  
Causal Discovery ◽  
Mediterranean Ecosystem ◽  
Interaction Patterns ◽  
Climate Data ◽  
Detection Rates ◽  
The Impact

Abstract. Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models.

Uni-Economic & Innovative Property Price Prediction in the HKSAR

2014 ◽  
Vol 2 (1) ◽  
pp. 54-75
Author(s):  
Samuel K. M. Ho ◽  
Marco C. K. Lau
Keyword(s):  
Time Series ◽  
Research Output ◽  
Medical Profession ◽  
Sunspot Cycle ◽  
Test Case ◽  
Exploratory Research ◽  
Property Market ◽  
Oil Crisis ◽  
Financial Indices ◽  
The Impact

Abstract Historically, the study of the world's economy was classified into Micro-economics and Macroeconomics. Perhaps, contemporary economists should learn from the 'astronomists' about the universe which we are part of. We shall name this 'Uni-economics'. Many scientists have found that sunspots affect human behaviour. Some research findings even relate the 11 year periodic cycle to war and peace of mankind. It is also widely-known in the medical profession that sunspot radiation actually affects the physiology of our human body. With all these evidence in mind, the aim of this exploratory research paper is to investigate how sunspot activities can affect the investors' sentiment in the financial world since 1970 when the first post-war financial crisis was built up resulting from the oil crisis in the early '1970s. Time series techniques were deployed to track down the changes of Sunspot Counts over the last 44 years and their impact on the world's 4 main financial indices, i.e., S&P, FTSE, Nikkei & HSI. It was pretty astonishing to find out that, whilst there are insignificant correlations amongst the 4 financial indices over the period under investigation, the impact of the Sunspot Counts on them are highly significant, even on a day-to-day time series analysis. As a corrolary of the finding, the HK Property Market is used as a test case for the research output. It was interesting to find out that, apart from the Solar Minima, the Change-of-Slope from positive to negative also has a significant impact on the HK property prices. Hence, it was evident that there are 2 property clashes during the 11 year sunspot cycle, and their timing can be predicted pretty accurately (2014 & 2019) within +/- one year.

scholarly journals Estimation of future groundwater recharge using climatic analogues and Hydrus-1D

2012 ◽  
Vol 9 (1) ◽  
pp. 1389-1410 ◽  
Author(s):  
B. Leterme ◽  
D. Mallants ◽  
D. Jacques
Keyword(s):  
Time Series ◽  
Water Balance ◽  
Groundwater Recharge ◽  
Balance Model ◽  
Temperate Climate ◽  
Subtropical Climate ◽  
Climate Data ◽  
Near Surface ◽  
The Impact ◽  
Hydrus 1D

Abstract. The impact of climate change on groundwater recharge is simulated using climatic analogue stations, i.e. stations presently under climatic conditions corresponding to a given climate state. The study was conducted in the context of a safety assessment of a future near-surface disposal facility for low and intermediate level short-lived radioactive waste in Belgium; this includes estimating groundwater recharge for the next millennia. Groundwater recharge was simulated using the Richard's based soil water balance model Hydrus-1D and meteorological time series from analogue stations. Water balance calculations showed that transition from a temperate oceanic to a warmer subtropical climate without rainfall seasonality is expected to yield a decrease in groundwater recharge (−12% for the chosen representative analogue station of Gijon, Northern Spain). Based on a time series of 24 yr of daily climate data, the long-term average annual recharge decreased from 314 to 276 mm, although total rainfall was higher (947 mm) in the warmer climate compared to the current temperate climate (899 mm). This is due to a higher soil evaporation (233 mm versus 206 mm) and higher plant transpiration (350 versus 285 mm) under the warmer climate.

scholarly journals Calibration of the modified Bartlett-Lewis model using global optimization techniques and alternative objective functions

2011 ◽  
Vol 8 (6) ◽  
pp. 9707-9756 ◽  
Author(s):  
W. J. Vanhaute ◽  
S. Vandenberghe ◽  
K. Scheerlinck ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Global Optimization ◽  
Optimization Methods ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Rainfall Time Series ◽  
Stochastic Point Process ◽  
Shuffled Complex Evolution ◽  
Global Optimization Methods ◽  
Downhill Simplex

Abstract. The use of rainfall time series for various applications is widespread. However, in many cases historical rainfall records lack in length or quality for certain practical purposes, resulting in a reliance on rainfall models to supply simulated rainfall time series, e.g., in the design of hydraulic structures. One way to obtain such simulations is by means of stochastic point process rainfall models, such as the Bartlett-Lewis type of model. It is widely acknowledged that the calibration of such models suffers from the presence of multiple local minima which local search algorithms usually fail to avoid. To meet this shortcoming, four relatively new global optimization methods are presented and tested for their abilities to calibrate the Modified Bartlett-Lewis Model (MBL). The list of tested methods consists of: the Downhill Simplex Method (DSM), Simplex-Simulated Annealing (SIMPSA), Particle Swarm Optimization (PSO) and Shuffled Complex Evolution (SCE-UA). The parameters of these algorithms are first optimized to ensure optimal performance, after which they are used for calibration of the MBL model. Furthermore, this paper addresses the issue of subjectivity in the choice of weights in the objective function. Three alternative weighing methods are compared to determine whether or not simulation results (obtained after calibration with the best optimization method) are influenced by the choice of weights.

scholarly journals Bayesian time-series analyses reveal a complex impact of climate change on Classic Maya conflict

2019 ◽  
Author(s):  
William Carleton ◽  
Dave Campbell ◽  
Mark Collard
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Classic Maya ◽  
Classic Period ◽  
Climate Data ◽  
Late Classic ◽  
Impact Of Climate Change ◽  
Increasing Temperature ◽  
Bayesian Time Series ◽  
The Impact

Researchers disagree about the impact of climate change on conflict among the Maya during the Classic period (ca. 250-900 CE). Some contend that increasing aridity exacerbated conflict, while others have found that increasing temperature ramped up conflict. Here, we report a study in which we sought to resolve this disagreement. We collated annually-resolved conflict and climate data, and then created a Bayesian time-series model for analysing count-based prehistoric and historic data. We carried out three analyses, one covering more or less the whole of the Classic Period (292-900 CE), one focused on the Early Classic (292-600 CE), and one that concentrated on the Late Classic (600-900 CE). Our analyses indicated that climate change likely did impact Classic Maya conflict levels, but our results differed from those of previous studies in two important ways. First, we found that the impact of climate change is only evident during the Late Classic. Second, we found that while increasing summer temperature exacerbated conflict, increasing aridity suppressed it. Thus, our study offers a new, more complex perspective on Classic Maya climate-conflict dynamics. It also has implications for our understanding of other aspects of Classic Maya history and for the debate about the likely impact of the current bout of climate change on conflict levels.

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